Abstract
We introduce the Neural Collaborative Subspace Clustering, a neural model that discovers clusters of data points drawn from a union of low-dimensional subspaces. In contrast to previous attempts, our model runs without the aid of spectral clustering. This makes our algorithm one of the kinds that can gracefully scale to large datasets. At its heart, our neural model benefits from a classifier which determines whether a pair of points lies on the same subspace or not. Essential to our model is the construction of two affinity matrices, one from the classifier and one based on a notion of subspace self-expressiveness, to supervise training in a collaborative scheme. We thoroughly assess and contrast the performance of our model against various state-of-the-art clustering algorithms including deep subspace-based ones.
Original language | English |
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Title of host publication | Proceedings of the 36th International Conference on Machine Learning |
Editors | Kamalika Chaudhuri, Ruslan Salakhutdinov |
Place of Publication | Stroudsburg PA USA |
Publisher | International Machine Learning Society (IMLS) |
Pages | 7384-7393 |
Number of pages | 10 |
Volume | 97 |
ISBN (Electronic) | 9781510886988 |
Publication status | Published - 2019 |
Event | International Conference on Machine Learning 2019 - Long Beach, United States of America Duration: 9 Jun 2019 → 15 Jun 2019 Conference number: 36th https://icml.cc/Conferences/2019 (Website) http://proceedings.mlr.press/v97/ (Proceedings) |
Publication series
Name | 36th International Conference on Machine Learning, ICML 2019 |
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Volume | 2019-June |
Conference
Conference | International Conference on Machine Learning 2019 |
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Abbreviated title | ICML 2019 |
Country/Territory | United States of America |
City | Long Beach |
Period | 9/06/19 → 15/06/19 |
Internet address |
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